Food insecurity remains a persistent public health emergency in the United States, tightly interwoven with chronic disease, mental illness, and opioid misuse. Yet despite the existence of thousands of food banks and pantries, access remains fragmented: 1) current retrieval systems depend on static directories or generic search engines, which provide incomplete and geographically irrelevant results; 2) LLM-based chatbots offer only vague nutritional suggestions and fail to adapt to real-world constraints such as time, mobility, and transportation; and 3) existing food recommendation systems optimize for culinary diversity but overlook survival-critical needs of food-insecure populations, including immediate proximity, verified availability, and contextual barriers. These limitations risk leaving the most vulnerable individuals, those experiencing homelessness, addiction, or digital illiteracy, unable to access urgently needed resources. To address this, we introduce Food4All, the first multi-agent framework explicitly designed for real-time, context-aware free food retrieval. Food4All unifies three innovations: 1) heterogeneous data aggregation across official databases, community platforms, and social media to provide a continuously updated pool of food resources; 2) a lightweight reinforcement learning algorithm trained on curated cases to optimize for both geographic accessibility and nutritional correctness; and 3) an online feedback loop that dynamically adapts retrieval policies to evolving user needs. By bridging information acquisition, semantic analysis, and decision support, Food4All delivers nutritionally annotated and guidance at the point of need. This framework establishes an urgent step toward scalable, equitable, and intelligent systems that directly support populations facing food insecurity and its compounding health risks.

翻译：食物不安全仍是美国持续存在的公共卫生紧急事件，与慢性疾病、精神疾病和阿片类药物滥用紧密交织。然而，尽管存在数千家食物银行和食品分发点，获取途径仍然支离破碎：1）当前的检索系统依赖于静态目录或通用搜索引擎，提供的结果既不完整又缺乏地理相关性；2）基于大语言模型的聊天机器人只能给出模糊的营养建议，且无法适应时间、行动能力和交通等现实约束条件；3）现有的食物推荐系统以烹饪多样性为优化目标，却忽视了食物不安全人群生存关键需求，包括即时邻近性、已验证可获取性和情境性障碍。这些局限性可能导致最脆弱的群体——包括无家可归者、成瘾者或数字文盲——无法获取急需的资源。为解决这一问题，我们提出了Food4All——首个专门用于实时、情境感知免费食物检索的多智能体框架。Food4All融合了三项创新：1）跨官方数据库、社区平台和社交媒体的异构数据聚合，提供持续更新的食物资源池；2）一种轻量级强化学习算法，基于 curated 案例训练，同时优化地理可及性和营养正确性；3）在线反馈回路，可动态调整检索策略以适应不断变化的用户需求。通过衔接信息获取、语义分析和决策支持，Food4All在需求点提供带营养注释的指导。该框架为构建可扩展、公平且智能的系统迈出了紧迫一步，直接支持面临食物不安全及其复合健康风险的人群。